Opioid epidemic may have new nemesis in AI-based ‘deep reinforcement learning’
Deep reinforcement learning enabled by AI can help physicians figure out whether opioids would truly be better than other interventions for patients suffering pain in intensive care units.
Research presenting the finding was presented at a conference in Europe last week, and Psychology Today breaks down the details in an article posted online July 25.
“Deep reinforcement learning has contributed to AI milestone achievements in chess, various Atari games, shogi (Japanese chess) and Go,” explains PT writer Cami Rosso. “In healthcare, deep reinforcement learning has been applied toward chemotherapy dosing for cancer patients in clinical trials, ICU heparin dosing and the dosing of intravenous fluids and vasopressor for sepsis patients, among other purposes.”
In the new study, PhD candidate Daniel Lopez-Martinez of MIT and colleagues used data from more than 40,000 hospitalizations to understand what safe and efficacious opioid interventions should look like.
The team’s algorithm identified optimal actions for pain management, personalizing them for each patient.
“We believe that, with additional work, we may be able to produce an AI system that … will help prevent opioid addictions and combat the opioid epidemic,” Lopez-Martinez says.
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Dave P. has worked in journalism, marketing and public relations for more than 30 years, frequently concentrating on hospitals, healthcare technology and Catholic communications. He has also specialized in fundraising communications, ghostwriting for CEOs of local, national and global charities, nonprofits and foundations.